{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Домашняя работа \"Оценка точности модели, переобучение, регуляризация\"\n",
    "\n",
    "- Посчитать tpr и fpr в ноутбуке с лекции. Убедиться, что график ROC AUC получается таким же, как и штатными средствами\n",
    "\n",
    "- Построить график Precision - Recall на этих же данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, precision_recall_curve\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_logistic_predict(y_proba, thresh=0.5):\n",
    "    \"\"\" Функция для двоичной классификации на основе вероятностей принадлежности к классу 1.\n",
    "    \n",
    "    Параметры\n",
    "    ---------\n",
    "    y_proba : array\n",
    "      Список вероятностей принадлежности к классу 1\n",
    "    thresh : float\n",
    "      Порог классификации, все вероятности выше порога будут относиться к классу 1\n",
    "      \n",
    "    Результат\n",
    "    ---------\n",
    "    y_binary : array\n",
    "      Список категорий для y_proba, список содержит либо 1, либо 0\n",
    "    \"\"\"\n",
    "    y_proba = np.array(y_proba)\n",
    "    y_binary = np.where(y_proba > thresh, 1, 0)\n",
    "    \n",
    "    return y_binary\n",
    "\n",
    "\n",
    "def calculate_roc_curve(y_proba, y):\n",
    "    \"\"\" Функция для расчета TRP и FRP функции ошибок ROC двоичного классификатора\n",
    "    для разных порогов классификации\n",
    "    \n",
    "    Параметры\n",
    "    ---------\n",
    "    y_proba : array\n",
    "      Список вероятностей принадлежности к классу 1\n",
    "    y : array\n",
    "      Список бинарных категорий\n",
    "      \n",
    "    Результат\n",
    "    ---------\n",
    "    tpr, frp : tuple\n",
    "      Значния TRP и FRP для разных порогов классификации\n",
    "    \"\"\"\n",
    "    \n",
    "    thresh_values = np.linspace(0, 1, 1000)\n",
    "    \n",
    "    tpr = []\n",
    "    frp = []\n",
    "    \n",
    "    for thresh_value in thresh_values:\n",
    "        tp_value = 0\n",
    "        fp_value = 0\n",
    "        tn_value = 0\n",
    "        fn_value = 0\n",
    "        y_predict_binary = binary_logistic_predict(y_proba=y_proba, thresh=thresh_value)\n",
    "        \n",
    "        for y_predict, y_real in zip(y_predict_binary, y):\n",
    "            \n",
    "            if y_predict == 1:\n",
    "                \n",
    "                if y_real == 1:\n",
    "                    tp_value += 1\n",
    "                else:\n",
    "                    fp_value += 1 \n",
    "                    \n",
    "            elif y_predict == 0:\n",
    "                \n",
    "                if y_real == 0:\n",
    "                    tn_value += 1  \n",
    "                else:\n",
    "                    fn_value += 1  \n",
    "        \n",
    "        tpr.append(tp_value / np.clip(tp_value + fn_value, 1e-12, np.inf))\n",
    "        frp.append(fp_value / np.clip(fp_value + tn_value, 1e-12, np.inf))    \n",
    "        \n",
    "    return frp, tpr\n",
    "\n",
    "\n",
    "def calculate_precision_recal_curve(y_proba, y):\n",
    "    \"\"\" Функция для расчета precision и recal двоичного классификатора для разных порогов классификации\n",
    "    \n",
    "    Параметры\n",
    "    ---------\n",
    "    y_proba : array\n",
    "      Список вероятностей принадлежности к классу 1\n",
    "    y : array\n",
    "      Список бинарных категорий\n",
    "      \n",
    "    Результат\n",
    "    ---------\n",
    "    precision, recal : tuple\n",
    "      Значния precision и recal для разных порогов классификации\n",
    "    \"\"\"\n",
    "    \n",
    "    thresh_values = np.linspace(0, 1, 1000)\n",
    "    \n",
    "    precision = []\n",
    "    recall = []\n",
    "    \n",
    "    for thresh_value in thresh_values:\n",
    "        tp_value = 0\n",
    "        fp_value = 0\n",
    "        tn_value = 0\n",
    "        fn_value = 0\n",
    "        y_predict_binary = binary_logistic_predict(y_proba=y_proba, thresh=thresh_value)\n",
    "        \n",
    "        for y_predict, y_real in zip(y_predict_binary, y):\n",
    "            \n",
    "            if y_predict == 1:\n",
    "                \n",
    "                if y_real == 1:\n",
    "                    tp_value += 1\n",
    "                else:\n",
    "                    fp_value += 1 \n",
    "                    \n",
    "            elif y_predict == 0:\n",
    "                \n",
    "                if y_real == 0:\n",
    "                    tn_value += 1  \n",
    "                else:\n",
    "                    fn_value += 1  \n",
    "        \n",
    "        precision.append(tp_value / np.clip(tp_value + fp_value, 1e-12, np.inf))\n",
    "        recall.append(tp_value / np.clip(tp_value + fn_value, 1e-12, np.inf))    \n",
    "        \n",
    "    return precision, recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
       "0            3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
       "1            3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
       "2            4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
       "3            4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
       "4            5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
       "\n",
       "   occupation_husb  affair  \n",
       "0              5.0       1  \n",
       "1              4.0       1  \n",
       "2              5.0       1  \n",
       "3              5.0       1  \n",
       "4              4.0       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('affair_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[data.columns.drop('affair')]\n",
    "y = data['affair']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_proba = lr.predict_proba(X_test)[:, 1]\n",
    "frp, tpr = calculate_roc_curve(y_proba=y_proba, y=y_test)\n",
    "\n",
    "frp_sk, tpr_sk, thresh_sk = roc_curve(y_true=y_test, y_score=y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(frp, tpr)\n",
    "plt.title('[РУЧНОЙ] График зависимости TPR и FPR от разных уровней порогов')\n",
    "plt.xlabel('FPR')\n",
    "plt.xlabel('TPR')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(frp_sk, tpr_sk)\n",
    "plt.title('[SKLEARN] График зависимости TPR и FPR от разных уровней порогов')\n",
    "plt.xlabel('FPR')\n",
    "plt.xlabel('TPR')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall = calculate_precision_recal_curve(y_proba=y_proba, y=y_test)\n",
    "precision_sk, recall_sk, thresh_sk = precision_recall_curve(y_true=y_test, probas_pred=y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(recall, precision)\n",
    "plt.title('[РУЧНОЙ] График зависимости recal-precission')\n",
    "plt.xlabel('recall')\n",
    "plt.xlabel('precision')\n",
    "plt.xticks(np.linspace(0, 1, 10))\n",
    "plt.yticks(np.linspace(0, 1, 10))\n",
    "plt.show()\n",
    "\n",
    "plt.plot(recall_sk, precision_sk)\n",
    "plt.title('[SKLEARN] График зависимости recal-precission')\n",
    "plt.xlabel('recall')\n",
    "plt.xlabel('precision')\n",
    "plt.xticks(np.linspace(0, 1, 10))\n",
    "plt.yticks(np.linspace(0, 1, 10))\n",
    "plt.show()"
   ]
  }
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